{
  "schema_version": "2.0",
  "slug": "cjo4m06-mcp-shrimp-task-manager",
  "name": "Shrimp Task Manager",
  "agent_url": "https://cjo4m06.github.io/mcp-shrimp-task-manager/",
  "category": "Coding",
  "run_id": "run-r-publish-v2-cjo4m06-mcp-shrimp-task-manager-2026-08-26",
  "run_at": "2026-08-26T09:00:00Z",
  "editor": "Hlido Editor",
  "editorial_method": "public-surface-tier-1+editorial-narrative-v2",
  "methodology_version": "2026.05",
  "methodology_url": "/methodology/public-surface-tier-1/",
  "score": 75,
  "tier": "STEADY",
  "laddoo_score": 75,
  "confidence": "medium",
  "hlido_opinion": {
    "headline": "An MCP task manager that gives AI coding assistants long-term memory, structured task decomposition with dependencies, and execution tracking — aimed squarely at the 'assistant forgets across conversations' problem.",
    "body": "Shrimp Task Manager names its target precisely: the three ways AI programming assistants fail at multi-step work — memory loss across conversations, structural chaos in complex tasks, and starting every session from scratch — and answers each with a concrete feature. It provides a task-memory function that persists execution history, automatic decomposition of complex tasks into subtasks with explicit dependencies and ordered execution paths, real-time execution-status tracking with progress visualization, and a knowledge-accumulation store that records successful solutions for reuse. Delivered as an MCP server, it plugs into agentic coding assistants rather than being a standalone product, which is the right shape for the job. The documentation is well-structured (pain-points → six core features → workflow → prompt config → installation) and bilingual, and it is open source on GitHub. It lands mid-STEADY because the strengths are described capabilities rather than measured outcomes in this review, there is no independent benchmark of how well decomposition and dependency-tracking actually hold up on real tasks, and no adoption/maintenance signal was captured. As a structured-memory layer for a coding agent, though, it is coherent and well-scoped.",
    "voice": "Hlido Editor",
    "as_of": "2026-08-26",
    "editor_signature_pending": true
  },
  "tier_rationale": "STEADY (75) because Shrimp Task Manager maps three real failure modes of AI coding assistants to concrete features — persistent task memory, dependency-aware decomposition, execution tracking, knowledge reuse — delivered agent-native over MCP with clear docs. Held mid-STEADY because these are described capabilities, not outcomes measured in this review, with no independent benchmark or captured adoption/maintenance signal.",
  "what_it_does_well": [
    "Persistent task memory across conversations — directly targets the assistant's cross-session amnesia",
    "Automatic decomposition of complex tasks into subtasks with explicit dependencies and ordered execution",
    "Real-time execution-status tracking with progress visualization and completion reports",
    "Knowledge-accumulation store that records successful solutions for reuse on similar tasks",
    "Agent-native as an MCP server; clear, well-structured, bilingual documentation and prompt config",
    "Open source on GitHub"
  ],
  "what_it_fails_at": [
    "Strengths are described features, not outcomes measured in this review",
    "No independent benchmark of decomposition/dependency-tracking quality on real tasks",
    "No captured adoption, maintenance-cadence or license detail on the reviewed surface",
    "Value depends on the host assistant honouring the structured plan it produces"
  ],
  "best_for": [
    "Developers whose AI coding assistant loses track of multi-step tasks across a session",
    "Agentic workflows that need explicit task decomposition, dependencies and progress tracking",
    "Teams that want a reusable knowledge base of solved tasks feeding future prompts"
  ],
  "not_recommended_for": [
    "Users wanting a standalone project-management app rather than an MCP layer for an assistant",
    "Teams that require proven, benchmarked reliability guarantees today"
  ],
  "red_flags": [],
  "compared_to": [
    {
      "slug": "mybono-ai-orchestrator",
      "verdict_diff": "Both structure multi-step agent work; the orchestrator coordinates agents/flows broadly, while Shrimp is a focused MCP memory-and-decomposition layer for a coding assistant. Orchestrator for cross-agent flow control, Shrimp for in-assistant task memory and breakdown.",
      "preferred_for_axis": "in-assistant-task-memory-vs-cross-agent-orchestration"
    },
    {
      "slug": "deusdata-codebase-memory-mcp",
      "verdict_diff": "codebase-memory-mcp persists knowledge about the codebase; Shrimp persists task state and decomposition. Codebase-memory for what the code is, Shrimp for what the assistant is doing to it.",
      "preferred_for_axis": "task-state-memory-vs-codebase-memory"
    }
  ],
  "evidence_urls": [
    {
      "claim": "Provides long-term/task memory so assistants remember progress across conversations",
      "source": "cjo4m06.github.io/mcp-shrimp-task-manager ('Task Memory Function ... provide long-term memory capability, allowing AI assistants to remember previous task progress')",
      "tested_at": "2026-08-26",
      "verified": true
    },
    {
      "claim": "Automatically decomposes complex tasks into subtasks with dependencies and ordered execution",
      "source": "cjo4m06.github.io/mcp-shrimp-task-manager ('Structured Task Decomposition ... establish clear dependencies, provide ordered execution paths')",
      "tested_at": "2026-08-26",
      "verified": true
    },
    {
      "claim": "Six core features incl. execution-status tracking and knowledge accumulation",
      "source": "cjo4m06.github.io/mcp-shrimp-task-manager ('six core features'; 'Execution Status Tracking'; 'Knowledge Accumulation & Experience Reference')",
      "tested_at": "2026-08-26",
      "verified": true
    }
  ],
  "agent_relevance": {
    "has_api": false,
    "has_cli": false,
    "has_mcp": true,
    "has_webhook": false,
    "has_sdk": false,
    "behavioral_testable": true,
    "agent_integration_path": "Runs as an MCP server connected to an AI coding assistant; the assistant calls it to plan and decompose tasks, persist task memory across sessions, track execution status, and reference a knowledge base of prior solutions. It is a structured-memory-and-planning layer for the agent rather than a standalone tool.",
    "agent_friendly_score": 8
  },
  "checklist": [
    {
      "id": "homepage_loads",
      "pass": true,
      "required": true,
      "tested_at": "2026-08-26T09:00:00Z"
    },
    {
      "id": "primary_value_prop",
      "pass": true,
      "required": true,
      "evidence": "Structured task management + long-term memory for AI programming assistants",
      "tested_at": "2026-08-26T09:00:00Z"
    },
    {
      "id": "cta_present",
      "pass": true,
      "required": true,
      "evidence": "Get Started / Installation / GitHub",
      "tested_at": "2026-08-26T09:00:00Z"
    },
    {
      "id": "pricing_or_access",
      "pass": true,
      "required": false,
      "evidence": "Open source on GitHub; MCP install",
      "tested_at": "2026-08-26T09:00:00Z"
    },
    {
      "id": "evidence_or_demo",
      "pass": true,
      "required": false,
      "evidence": "Pain-points, six-feature breakdown, workflow and prompt-config docs",
      "tested_at": "2026-08-26T09:00:00Z"
    }
  ],
  "summary": "An MCP task manager that gives AI coding assistants long-term memory, structured task decomposition with dependencies, and execution tracking — aimed squarely at the 'assistant forgets across conversations' problem.",
  "_summary_deprecation_note": "Field kept as a v1-compatibility alias of hlido_opinion.headline. New consumers should read hlido_opinion.{headline,body,voice,as_of}.",
  "staleness_after": "2026-11-24",
  "review_age_days_at_publish": 0,
  "next_review_due_at": "2026-11-24",
  "attestation_url": "/data/attestations/cjo4m06-mcp-shrimp-task-manager.json",
  "signature_pending": true,
  "source": "r-publish-editorial-v2",
  "marking_signal": {
    "checked_at": "2026-08-26",
    "source": "r-publish-editorial-enrich",
    "not_applicable": true,
    "note": "Shrimp Task Manager manages task state and memory for a coding assistant; it does not generate synthetic media for publication. Article-50 marking obligations do not apply. Recorded as not applicable."
  },
  "evidence_images": {
    "run_id": "run-a98709bb99713d17-cjo4m06-github-io",
    "base": "https://images.hlido.eu/reviews/cjo4m06-mcp-shrimp-task-manager/run-a98709bb99713d17-cjo4m06-github-io",
    "files": [
      "home.png",
      "page_pain-points.png",
      "page_features.png",
      "page_workflow.png"
    ],
    "urls": [
      "https://images.hlido.eu/reviews/cjo4m06-mcp-shrimp-task-manager/run-a98709bb99713d17-cjo4m06-github-io/home.png",
      "https://images.hlido.eu/reviews/cjo4m06-mcp-shrimp-task-manager/run-a98709bb99713d17-cjo4m06-github-io/page_pain-points.png",
      "https://images.hlido.eu/reviews/cjo4m06-mcp-shrimp-task-manager/run-a98709bb99713d17-cjo4m06-github-io/page_features.png",
      "https://images.hlido.eu/reviews/cjo4m06-mcp-shrimp-task-manager/run-a98709bb99713d17-cjo4m06-github-io/page_workflow.png"
    ],
    "note": "Screenshots captured by the Hlido engine during the reviewed run, served from R2. `run_id` is the ENGINE run id — it differs from `scorecard.run_id` and is the only one these keys resolve under."
  },
  "pricing_facts": {
    "schema": "pricing-facts/1",
    "model": [
      "open-source"
    ],
    "free_tier": true,
    "pricing_disclosed": {
      "pass": true,
      "evidence": "Open source on GitHub; MCP install",
      "tested_at": "2026-08-26"
    },
    "last_verified": "2026-08-26",
    "basis": "Derived from Hlido-held evidence only (engine checklist + editorial text); quotes are verbatim from the scorecard; not vendor-supplied; re-derived daily. Verify current prices on the vendor's pricing page.",
    "derived_at": "2026-08-27"
  }
}